# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Basic word2vec example."""
#coding:utf-8
#from __future__ import absolute_import
#from __future__ import division
#from __future__ import print_function
import sklearn
import matplotlib
#matplotlib.use('qt4agg')
import collections
import math
import os
import sys
import json
import argparse
import random
from tempfile import gettempdir
#import zipfile

import numpy as np
#from six.moves import urllib
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

import utils
from tensorflow.contrib.tensorboard.plugins import projector

# Give a folder path as an argument with '--log_dir' to save
# TensorBoard summaries. Default is a log folder in current directory.
current_path = os.path.dirname(os.path.realpath(sys.argv[0]))

parser = argparse.ArgumentParser()
parser.add_argument(
    '--log_dir',
    type=str,
    default=os.path.join(current_path, 'log'),
    help='The log directory for TensorBoard summaries.')
FLAGS, unparsed = parser.parse_known_args()

# Create the directory for TensorBoard variables if there is not.
if not os.path.exists(FLAGS.log_dir):
  os.makedirs(FLAGS.log_dir)

# Step 1: Download the data.
def load_file(filename, file_url):
    local_filename = os.path.join(file_url, filename)
    return utils.read_data(local_filename)


quanSongCi = load_file('QuanSongCi.txt', '')


# pylint: disable=redefined-outer-name

print('Data size', len(quanSongCi))

# Step 2: Build the dictionary and replace rare words with UNK token.
vocabulary_size = 5000

# Filling 4 global variables:
# data - list of codes (integers from 0 to vocabulary_size-1).
#   This is the original text but words are replaced by their codes
# count - map of words(strings) to count of occurrences
# dictionary - map of words(strings) to their codes(integers)
# reverse_dictionary - maps codes(integers) to words(strings)
data, count, dictionary, reverse_dictionary = utils.build_dataset(
    quanSongCi, vocabulary_size)
del quanSongCi  # Hint to reduce memory.
print('Most common words (+UNK)', count[:5])
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])

with open('dictionary' + '.json', 'w', encoding='utf8') as f:
    json.dump(dictionary, f, ensure_ascii=False)

with open('reverse_dictionary' + '.json', 'w', encoding='utf8') as f:
    json.dump(reverse_dictionary, f, ensure_ascii=False)

data_index = 0


# Step 3: Function to generate a training batch for the skip-gram model.
def generate_batch(batch_size, num_skips, skip_window):
  global data_index
  assert batch_size % num_skips == 0
  assert num_skips <= 2 * skip_window
  batch = np.ndarray(shape=(batch_size), dtype=np.int32)
  labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
  span = 2 * skip_window + 1  # [ skip_window target skip_window ]
  buffer = collections.deque(maxlen=span)  # pylint: disable=redefined-builtin
  if data_index + span > len(data):
    data_index = 0
  buffer.extend(data[data_index:data_index + span])
  data_index += span
  for i in range(batch_size // num_skips):
    context_words = [w for w in range(span) if w != skip_window]
    words_to_use = random.sample(context_words, num_skips)
    for j, context_word in enumerate(words_to_use):
      batch[i * num_skips + j] = buffer[skip_window]
      labels[i * num_skips + j, 0] = buffer[context_word]
    if data_index == len(data):
      buffer.extend(data[0:span])
      data_index = span
    else:
      buffer.append(data[data_index])
      data_index += 1
  # Backtrack a little bit to avoid skipping words in the end of a batch
  data_index = (data_index + len(data) - span) % len(data)
  return batch, labels


batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)
for i in range(8):
  print(batch[i], reverse_dictionary[batch[i]], '->', labels[i, 0],
        reverse_dictionary[labels[i, 0]])

# Step 4: Build and train a skip-gram model.

batch_size = 128
embedding_size = 128  # Dimension of the embedding vector.
skip_window = 1  # How many words to consider left and right.
num_skips = 2  # How many times to reuse an input to generate a label.
num_sampled = 64  # Number of negative examples to sample.

# We pick a random validation set to sample nearest neighbors. Here we limit the
# validation samples to the words that have a low numeric ID, which by
# construction are also the most frequent. These 3 variables are used only for
# displaying model accuracy, they don't affect calculation.
valid_size = 16  # Random set of words to evaluate similarity on.
valid_window = 100  # Only pick dev samples in the head of the distribution.
valid_examples = np.random.choice(valid_window, valid_size, replace=False)

graph = tf.Graph()

with graph.as_default():

  # Input data.
  with tf.name_scope('inputs'):
    train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)

  # Ops and variables pinned to the CPU because of missing GPU implementation
  with tf.device('/cpu:0'):
    # Look up embeddings for inputs.
    with tf.name_scope('embeddings'):
      embeddings = tf.Variable(
          tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
      embed = tf.nn.embedding_lookup(embeddings, train_inputs)

    # Construct the variables for the NCE loss
    with tf.name_scope('weights'):
      nce_weights = tf.Variable(
          tf.truncated_normal(
              [vocabulary_size, embedding_size],
              stddev=1.0 / math.sqrt(embedding_size)))
    with tf.name_scope('biases'):
      nce_biases = tf.Variable(tf.zeros([vocabulary_size]))

  # Compute the average NCE loss for the batch.
  # tf.nce_loss automatically draws a new sample of the negative labels each
  # time we evaluate the loss.
  # Explanation of the meaning of NCE loss:
  #   http://mccormickml.com/2016/04/19/word2vec-tutorial-the-skip-gram-model/
  with tf.name_scope('loss'):
    loss = tf.reduce_mean(
        tf.nn.nce_loss(
            weights=nce_weights,
            biases=nce_biases,
            labels=train_labels,
            inputs=embed,
            num_sampled=num_sampled,
            num_classes=vocabulary_size))

  # Add the loss value as a scalar to summary.
  tf.summary.scalar('loss', loss)

  # Construct the SGD optimizer using a learning rate of 1.0.
  with tf.name_scope('optimizer'):
    optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)

  # Compute the cosine similarity between minibatch examples and all embeddings.
  norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keepdims=True))
  normalized_embeddings = embeddings / norm
  valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings,
                                            valid_dataset)
  similarity = tf.matmul(
      valid_embeddings, normalized_embeddings, transpose_b=True)

  # Merge all summaries.
  merged = tf.summary.merge_all()

  # Add variable initializer.
  init = tf.global_variables_initializer()

  # Create a saver.
  saver = tf.train.Saver()

# Step 5: Begin training.
num_steps = 440000

with tf.Session(graph=graph) as session:
  # Open a writer to write summaries.
  writer = tf.summary.FileWriter(FLAGS.log_dir, session.graph)

  # We must initialize all variables before we use them.
  init.run()
  print('Initialized')

  average_loss = 0
  for step in xrange(num_steps):
    batch_inputs, batch_labels = generate_batch(batch_size, num_skips,
                                                skip_window)
    feed_dict = {train_inputs: batch_inputs, train_labels: batch_labels}

    # Define metadata variable.
    run_metadata = tf.RunMetadata()

    # We perform one update step by evaluating the optimizer op (including it
    # in the list of returned values for session.run()
    # Also, evaluate the merged op to get all summaries from the returned "summary" variable.
    # Feed metadata variable to session for visualizing the graph in TensorBoard.
    _, summary, loss_val = session.run(
        [optimizer, merged, loss],
        feed_dict=feed_dict,
        run_metadata=run_metadata)
    average_loss += loss_val

    # Add returned summaries to writer in each step.
    writer.add_summary(summary, step)
    # Add metadata to visualize the graph for the last run.
    if step == (num_steps - 1):
      writer.add_run_metadata(run_metadata, 'step%d' % step)

    if step % 2000 == 0:
      if step > 0:
        average_loss /= 2000
      # The average loss is an estimate of the loss over the last 2000 batches.
      print('Average loss at step ', step, ': ', average_loss)
      average_loss = 0

    # Note that this is expensive (~20% slowdown if computed every 500 steps)
    if step % 10000 == 0:
      sim = similarity.eval()
      for i in xrange(valid_size):
        valid_word = reverse_dictionary[valid_examples[i]]
        top_k = 8  # number of nearest neighbors
        nearest = (-sim[i, :]).argsort()[1:top_k + 1]
        log_str = 'Nearest to %s:' % valid_word
        for k in xrange(top_k):
          close_word = reverse_dictionary[nearest[k]]
          log_str = '%s %s,' % (log_str, close_word)
        print(log_str)
  final_embeddings = normalized_embeddings.eval()
  np.save('embedding.npy', final_embeddings)

  # Write corresponding labels for the embeddings.
  with open(FLAGS.log_dir + '/metadata.tsv', 'w') as f:
    for i in xrange(vocabulary_size):
      f.write(reverse_dictionary[i] + '\n')

  # Save the model for checkpoints.
  saver.save(session, os.path.join(FLAGS.log_dir, 'model.ckpt'))

  # Create a configuration for visualizing embeddings with the labels in TensorBoard.
  config = projector.ProjectorConfig()
  embedding_conf = config.embeddings.add()
  embedding_conf.tensor_name = embeddings.name
  embedding_conf.metadata_path = os.path.join(FLAGS.log_dir, 'metadata.tsv')
  projector.visualize_embeddings(writer, config)

writer.close()

# Step 6: Visualize the embeddings.


# pylint: disable=missing-docstring
# Function to draw visualization of distance between embeddings.
def plot_with_labels(low_dim_embs, labels, filename):
  assert low_dim_embs.shape[0] >= len(labels), 'More labels than embeddings'
  plt.figure(figsize=(18, 18))  # in inches
  for i, label in enumerate(labels):
    x, y = low_dim_embs[i, :]
    plt.scatter(x, y)
    plt.annotate(
        label,
        xy=(x, y),
        xytext=(5, 2),
        textcoords='offset points',
        ha='right',
        va='bottom')

  plt.savefig(filename)


try:
  # pylint: disable=g-import-not-at-top
  from sklearn.manifold import TSNE
  import matplotlib.pyplot as plt
  from pylab import mpl
  import matplotlib
#matplotlib.use('qt4agg')
  
  matplotlib.rcParams['font.sans-serif'] = ['SimHei']
  matplotlib.rcParams['font.family']='sans-serif'
  mpl.rcParams['axes.unicode_minus'] = False  # 解决保存图像是负号'-'显示为方块的问题

  tsne = TSNE(
      perplexity=30, n_components=2, init='pca', n_iter=5000, method='exact')
  plot_only = 500
  low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
  labels = [reverse_dictionary[i] for i in xrange(plot_only)]
  plot_with_labels(low_dim_embs, labels, os.path.join(gettempdir(), 'tsne.png'))
  plt.show(tsne)

except ImportError as ex:
  print('Please install sklearn, matplotlib, and scipy to show embeddings.')
  print(ex)
